Finally, the statistical relationships identified by the RFs are inspected to glean additional insights about the features that the models rely on to make predictions and how those relationships align with our physical understanding of severe weather forecasting. Feature importances (FIs) are computed and evaluated to assess the use of predictor information in each tree of the forest. Specifically, the "Gini importance" metric [1]}, [2]}, [3]}, [4]} is used to quantify the FIs. Each feature is assigned an importance value based on the number of times it is used to split a decision tree node, weighted by the number of training examples at the split [5]}. The importances are then summed over all splits and trees in the forest and can be aggregated to characterize temporal or spatial importance patterns [4]}. The higher the importance, the more value the RFs place on that predictor to make predictions. While other FI techniques exist [7]}, the Gini importance metric is used here as an initial glance and not a holistic interrogation of FIs of the developed RFs; a follow-on manuscript is being prepared to fully interrogate model FIs with sufficient breadth and depth.
